Golden Path Search Algorithm for the KSA Scheme

Ching Kang Ing, Chin Yi Lin, Po Hsiang Peng, Yu Ming Hsieh, Fan Tien Cheng

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

The concepts of Industry 4.1 for achieving Zero-Defect (ZD) manufacturing were disclosed in IEEE Robotics and Automation Letters in January 2016. ZD of all the deliverables can be achieved by discarding the defective products via a real-time and online total inspection technology, such as Automatic Virtual Metrology (AVM). Further, the Key-variable Search Algorithm (KSA) of the Intelligent Yield Management (IYM) system developed by our research team can be utilized to find out the root causes of the defects for continuous improvement on those defective products. As such, nearly ZD of all products may be achieved. However, in a multistage manufacturing process (MMP) environment, a workpiece may randomly pass through one of the manufacturing devices with the same function in each stage. Different devices of the same type perform differently in each stage, where the performances will be accumulated through the designated manufacturing process and affect the final yield. KSA can only identify the influence of univariate variables (i.e., single devices) on the yield, yet it cannot detect the manufacturing paths that have significant influence on the yield. In order to cope with this deficiency such that the golden path with a better yield amongst all the MMP paths can be found, this research proposes the Golden Path Search Algorithm (GPSA), which can plan golden paths with high yield under the condition of the number of variables being much larger than that of samples. As a result, it makes the improvement of manufacturing yield be more comprehensive.

原文English
頁(從 - 到)1517-1529
頁數13
期刊IEEE Transactions on Automation Science and Engineering
19
發行號3
DOIs
出版狀態Published - 2022 7月 1

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 電氣與電子工程

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